At e-commerce websites, the effectiveness of search ranking significantly affects the searching and shopping user experiences. In search engine systems, many factors affect the ranking of search results. Such factors include user feedback and user search action data, which are iteratively collected and analyzed to improve the product ranking technique. These factors affect online ranking results using a specific combination of weightings. Conventionally, product search engines use one ranking model to rank products found for any search query. A ranking model includes weightings associated with respective features.
Typically, in product search result ranking models, assessment mechanisms are established targeting multiple ranking features, such as product quality, product text matching, product category click matching, product price matching, and product sales volume. Behind each assessment mechanism, all products are assessed by either an algorithmic model or expert knowledge, and the assessments are embodied in the form of feature score values with respect to the ranking features for the products. Score values comprise the fundamental features of the ranking model.
During online ranking in actual application of these fundamental features, targeting a product list returned by a specific query, the product scores which serve as the basis for product ranking are obtained based on determining the weighted sum of the feature score values of the product ranking features. Because the feature score values of different ranking features have different weightings, the determination of the weightings will effectively determine how products will be ranked on the search results page. In one example, the feature score values for product sales volume and product category could have higher weightings because those features represent content about which the user is directly concerned. In another example, a lower weighting may be assigned to the feature score value that assesses text matching.
Conventionally, all online ranking of products simultaneously use one set of rank weightings. This set of rank weightings is selected using a specialized knowledge of experts using A/B testing to perform online verification of the rank weightings. In the optimization of search engine ranking results, A/B testing refers to the technique of comparing the merits and drawbacks of different optimization algorithms. For example, all query traffic in the system is divided into a number of equal portions. One portion of the query traffic is selected to serve as the basic test traffic and the existing system algorithm is invoked to rank the search results. Then, user feedback action data associated with the product rankings by the existing system algorithm is obtained to compute ranking effectiveness of the existing system algorithm. For the other equal portions of query traffic, a new optimized algorithm is used to rank the search results. Then, user feedback action data is obtained to compute ranking effectiveness of the new optimized algorithm. In this way, through comparison testing of different algorithms over a period of time, the comparative ranking effectiveness results of different ranking algorithms can be obtained. Based on the comparative results, the weightings used by the search ranking algorithm with the best ranking effectiveness can be implemented.
However, this weightings selection technique has at least the following defects:
(1) Currently, once the weightings for existing features are determined, the weightings are typically not subsequently adjusted over time. However, as time passes, previously determined weightings may no longer suit the current products to be ranked in search results.
(2) As the scope of product coverage increases, one set of uniform online weightings may no longer satisfy the ranking of all products, and a single model is no longer able to fully maximize returns on products in all categories. In other words, the weightings that are appropriate for products in a certain industry or category may not be appropriate for products in another industry or category.
(3) Because online weightings are usually determined using the existing specialized knowledge of experts, the ranking model is unable to learn autonomously and update automatically. At the same time, the determination of each set of weightings must undergo multiple rounds of A/B testing, which, due to lack of experience and knowledge, could result in excessively lengthy testing time or poor test effectiveness. Moreover, during this process, vast development and testing resources are expended, while adjustment and testing of the weightings could potentially affect overall search returns.
All of the three problems above could result in poorer search ranking results and may thereby detrimentally affect online transactions.